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学者姓名：刘文犀
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Abstract ：
In semi-supervised semantic segmentation (SSS), weak-to-strong consistency regularization techniques are widely utilized in recent works, typically combined with input-level and feature-level perturbations. However, the integration between weak-to-strong consistency regularization and network perturbation has been relatively rare. We note several problems with existing network perturbations in SSS that may contribute to this phenomenon. By revisiting network perturbations, we introduce a new approach for network perturbation to expand the existing weak-to-strong consistency regularization for unlabeled data. Additionally, we present a volatile learning process for labeled data, which is uncommon in existing research. Building upon previous work that includes input-level and feature-level perturbations, we present MLPMatch (Multi-Level-Perturbation Match), an easy-to-implement and efficient framework for semi-supervised semantic segmentation. MLPMatch has been validated on the Pascal VOC and Cityscapes datasets, achieving state-of-the-art performance. Code is available from https://github.com/LlistenL/MLPMatch.
Keyword ：
Consistency regularization Consistency regularization Network perturbation Network perturbation Semantic segmentation Semantic segmentation Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Li, Sien , Wang, Tao , Hui, Ruizhe et al. Revisiting Network Perturbation for Semi-supervised Semantic Segmentation [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT XII , 2025 , 15042 : 157-171 . |
| MLA | Li, Sien et al. "Revisiting Network Perturbation for Semi-supervised Semantic Segmentation" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT XII 15042 (2025) : 157-171 . |
| APA | Li, Sien , Wang, Tao , Hui, Ruizhe , Liu, Wenxi . Revisiting Network Perturbation for Semi-supervised Semantic Segmentation . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT XII , 2025 , 15042 , 157-171 . |
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U-Net is a classic architecture for semantic segmentation. However, it has several limitations, such as difficulty in capturing complex images detail due to its simple U structure, long convergence time arising from fixed network parameters, and suboptimal efficacy in decoding and restoring multi-scale information. To deal with the above issues, we propose a Multiple U-shaped network (Multi-UNet) assuming that constructing appropriate U-shaped structure can achieve better segmentation performance. Firstly, inspired by the concept of connecting multiple similar blocks, our Multi-UNet consists of multiple U-block modules, with each succeeding module directly connected to the previous one to facilitate data transmission between different U structures. We refer to the original bridge connections of U-Net as Intra-U connections and introduce a new type of connection called Inter-U connections. These Inter-U connections aim to retain as much detailed information as possible, enabling effective detection of complex images. Secondly, while maintaining Mean Intersection over Union (Mean-IoU), the up-sampling of each U applies uniformly small channel values to reduce the number of model parameters. Thirdly, a Spatial-Channel Parallel Attention Fusion (SCPAF) module is designed at the initial layer of every subsampling module of U-block architecture. It enhances feature extraction and alleviate computational overhead associated with data transmission. Finally, we replace the final up-sampling module with Atrous Spatial Pyramid Pooling Head (ASPPHead) to accomplish seamless multi-scale feature extraction. Our experiments are compared and analyzed with advanced models on three public datasets, and it can be concluded that the universality and accuracy of Multi-UNet network are superior.
Keyword ：
Multiple U-shaped network Multiple U-shaped network Semantic segmentation Semantic segmentation U-net U-net
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| GB/T 7714 | Zhao, Qiangwei , Cao, Jingjing , Ge, Junjie et al. Multi-UNet: An effective Multi-U convolutional networks for semantic segmentation [J]. | KNOWLEDGE-BASED SYSTEMS , 2025 , 309 . |
| MLA | Zhao, Qiangwei et al. "Multi-UNet: An effective Multi-U convolutional networks for semantic segmentation" . | KNOWLEDGE-BASED SYSTEMS 309 (2025) . |
| APA | Zhao, Qiangwei , Cao, Jingjing , Ge, Junjie , Zhu, Qi , Chen, Xiaoming , Liu, Wenxi . Multi-UNet: An effective Multi-U convolutional networks for semantic segmentation . | KNOWLEDGE-BASED SYSTEMS , 2025 , 309 . |
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Crowd counting has drawn increasing attention across various fields. However, existing crowd counting tasks primarily focus on estimating the overall population, ignoring the behavioral and semantic information of different social groups within the crowd. In this paper, we aim to address a newly proposed research problem, namely fine-grained crowd counting, which involves identifying different categories of individuals and accurately counting them in static images. In order to fully leverage the categorical information in static crowd images, we propose a two-tier salient feature propagation module designed to sequentially extract semantic information from both the crowd and its surrounding environment. Additionally, we introduce a category difference loss to refine the feature representation by highlighting the differences between various crowd categories. Moreover, our proposed framework can adapt to a novel problem setup called few-example fine-grained crowd counting. This setup, unlike the original fine-grained crowd counting, requires only a few exemplar point annotations instead of dense annotations from predefined categories, making it applicable in a wider range of scenarios. The baseline model for this task can be established by substituting the loss function in our proposed model with a novel hybrid loss function that integrates point-oriented cross-entropy loss and category contrastive loss. Through comprehensive experiments, we present results in both the formulation and application of fine-grained crowd counting.
Keyword ：
Adaptation models Adaptation models Annotations Annotations contrastive learning contrastive learning Contrastive learning Contrastive learning Crowd counting Crowd counting Feature extraction Feature extraction few-example fine-grained crowd counting few-example fine-grained crowd counting fine-grained crowd counting fine-grained crowd counting Fuses Fuses Meteorology Meteorology Propagation losses Propagation losses Semantics Semantics Social groups Social groups Visualization Visualization
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| GB/T 7714 | Zhang, Meijing , Chen, Mengxue , Li, Qi et al. Category-Contrastive Fine-Grained Crowd Counting and Beyond [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 : 477-488 . |
| MLA | Zhang, Meijing et al. "Category-Contrastive Fine-Grained Crowd Counting and Beyond" . | IEEE TRANSACTIONS ON MULTIMEDIA 27 (2025) : 477-488 . |
| APA | Zhang, Meijing , Chen, Mengxue , Li, Qi , Chen, Yanchen , Lin, Rui , Li, Xiaolian et al. Category-Contrastive Fine-Grained Crowd Counting and Beyond . | IEEE TRANSACTIONS ON MULTIMEDIA , 2025 , 27 , 477-488 . |
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In recent years, notable advancements have been achieved in the realm of identifying IPbased Internet of Things (IoT) devices and events. Nevertheless, the majority of methods rely on extracting fingerprints or features from plain text IP-based packets, which limits their ability to accommodate heterogeneous IoT devices such as ZigBee and Z-Wave, and fails to address the challenge of limited traffic samples. To tackle these issues, we propose a novel approach based on IoT communication characteristics and featuring module extensibility. This method is presented to effectively identify IoT devices and events from non-IP heterogeneous IoT network traffic. To shield the differences caused by the heterogeneous IoT protocol, a heterogeneous sample extraction platform with an extensible structure is created to extract raw sequence samples from ZigBee and ZWave traffic, with potential for expansion to other protocols. To address the challenges arising from the scarcity of samples, a sample identification framework based on IoT communication characteristics is devised to create synthetic samples from the raw sequence samples, enabling concurrent processing of the raw and synthetic samples using an identification model featuring two separate sequence networks. Comparative assessments of our method against baseline sequence models and the latest techniques demonstrate the advantages of our approach in identifying non-IP heterogeneous IoT traffic. The experimental results indicate that our method achieves an average accuracy improvement of 29.7% compared to baseline models using only raw samples. Furthermore, our method shows improvements of 22.1%, 21.5%, and 21.8% in macro precision, macro recall, and macro F1-score, respectively, over the latest method.
Keyword ：
Heterogeneous Heterogeneous IoT IoT Non-IP Non-IP Traffic identification Traffic identification
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| GB/T 7714 | Chen, Yi , Lai, Junxu , Lin, Zhu et al. Identify devices and events from non-IP heterogeneous IoT network traffic [J]. | PEERJ COMPUTER SCIENCE , 2024 , 10 . |
| MLA | Chen, Yi et al. "Identify devices and events from non-IP heterogeneous IoT network traffic" . | PEERJ COMPUTER SCIENCE 10 (2024) . |
| APA | Chen, Yi , Lai, Junxu , Lin, Zhu , Zhang, Meijing , Liu, Wenxi . Identify devices and events from non-IP heterogeneous IoT network traffic . | PEERJ COMPUTER SCIENCE , 2024 , 10 . |
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Semantic segmentation is one of the directions in image research. It aims to obtain the contours of objects of interest, facilitating subsequent engineering tasks such as measurement and feature selection. However, existing segmentation methods still lack precision in class edge, particularly in multi -class mixed region. To this end, we present the Feature Enhancement Network (FE -Net), a novel approach that leverages edge label and pixel -wise weights to enhance segmentation performance in complex backgrounds. Firstly, we propose a Smart Edge Head (SE -Head) to process shallow -level information from the backbone network. It is combined with the FCN-Head and SepASPP-Head, located at deeper layers, to form a transitional structure where the loss weights gradually transition from edge labels to semantic labels and a mixed loss is also designed to support this structure. Additionally, we propose a pixel -wise weight evaluation method, a pixel -wise weight block, and a feature enhancement loss to improve training effectiveness in multi -class regions. FE -Net achieves significant performance improvements over baselines on publicly datasets Pascal VOC2012, SBD, and ATR, with best mIoU enhancements of 15.19%, 1.42% and 3.51%, respectively. Furthermore, experiments conducted on Pole&Hole match dataset from our laboratory environment demonstrate the superior effectiveness of FE -Net in segmenting defined key pixels.
Keyword ：
Edge label Edge label Key pixels Key pixels Multi-class mixed region Multi-class mixed region Pixel-wise weight Pixel-wise weight Semantic segmentation Semantic segmentation
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| GB/T 7714 | Zhao, Zhangyan , Chen, Xiaoming , Cao, Jingjing et al. FE-Net: Feature enhancement segmentation network [J]. | NEURAL NETWORKS , 2024 , 174 . |
| MLA | Zhao, Zhangyan et al. "FE-Net: Feature enhancement segmentation network" . | NEURAL NETWORKS 174 (2024) . |
| APA | Zhao, Zhangyan , Chen, Xiaoming , Cao, Jingjing , Zhao, Qiangwei , Liu, Wenxi . FE-Net: Feature enhancement segmentation network . | NEURAL NETWORKS , 2024 , 174 . |
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Domain Generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain. The source domains always require precise annotations, which can be cumbersome or even infeasible to obtain in practice due to the vast amount of data involved. Web data, namely web -crawled images, offers an opportunity to access large amounts of unlabeled images with rich style information, which can be leveraged to improve DG. From this perspective, we introduce a novel paradigm of DG, termed as Semi -Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close -set and open -set SSDG. The close -set SSDG is based on existing public DG datasets, while the open -set SSDG, built on the newly -collected web -crawled datasets, presents a novel yet realistic challenge that pushes the limits of current technologies. A natural approach of SSDG is to transfer knowledge from labeled data to unlabeled data via pseudo labeling, and train the model on both labeled and pseudo -labeled data for generalization. Since there are conflicting goals between domain -oriented pseudo labeling and out -of -domain generalization, we develop a pseudo labeling phase and a generalization phase independently for SSDG. Unfortunately, due to the large domain gap, the pseudo labels provided in the pseudo labeling phase inevitably contain noise, which has negative affect on the subsequent generalization phase. Therefore, to improve the quality of pseudo labels and further enhance generalizability, we propose a cyclic learning framework to encourage a positive feedback between these two phases, utilizing an evolving intermediate domain that bridges the labeled and unlabeled domains in a curriculum learning manner. Extensive experiments are conducted to validate the effectiveness of our method. It is worth highlighting that web -crawled images can promote domain generalization as demonstrated by the experimental results.
Keyword ：
Domain generalization Domain generalization Semi-supervised learning Semi-supervised learning Transfer learning Transfer learning Unsupervised domain adaptation Unsupervised domain adaptation
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| GB/T 7714 | Lin, Luojun , Xie, Han , Sun, Zhishu et al. Semi-supervised domain generalization with evolving intermediate domain [J]. | PATTERN RECOGNITION , 2024 , 149 . |
| MLA | Lin, Luojun et al. "Semi-supervised domain generalization with evolving intermediate domain" . | PATTERN RECOGNITION 149 (2024) . |
| APA | Lin, Luojun , Xie, Han , Sun, Zhishu , Chen, Weijie , Liu, Wenxi , Yu, Yuanlong et al. Semi-supervised domain generalization with evolving intermediate domain . | PATTERN RECOGNITION , 2024 , 149 . |
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Ultra-high resolution image segmentation poses a formidable challenge for UAVs with limited computation resources. Moreover, with multiple deployed tasks (e.g., mapping, localization, and decision making), the demand for a memory efficient model becomes more urgent. This letter delves into the intricate problem of achieving efficient and effective segmentation of ultra-high resolution UAV imagery, while operating under stringent GPU memory limitation. To address this problem, we propose a GPU memory-efficient and effective framework. Specifically, we introduce a novel and efficient spatial-guided high-resolution query module, which enables our model to effectively infer pixel-wise segmentation results by querying nearest latent embeddings from low-resolution features. Additionally, we present a memory-based interaction scheme with linear complexity to rectify semantic bias beneath the high-resolution spatial guidance via associating cross-image contextual semantics. For evaluation, we perform comprehensive experiments over public benchmarks under both conditions of small and large GPU memory usage limitations. Notably, our model gains around 3% advantage against SOTA in mIoU using comparable memory. Furthermore, we show that our model can be deployed on the embedded platform with less than 8 G memory like Jetson TX2.
Keyword ：
Aerial Systems: Perception and Autonomy Aerial Systems: Perception and Autonomy Autonomous aerial vehicles Autonomous aerial vehicles Deep Learning for Visual Perception Deep Learning for Visual Perception Graphics processing units Graphics processing units Image resolution Image resolution Memory management Memory management Semantics Semantics Semantic segmentation Semantic segmentation Spatial resolution Spatial resolution
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| GB/T 7714 | Li, Qi , Cai, Jiaxin , Luo, Jiexin et al. Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (2) : 1708-1715 . |
| MLA | Li, Qi et al. "Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery" . | IEEE ROBOTICS AND AUTOMATION LETTERS 9 . 2 (2024) : 1708-1715 . |
| APA | Li, Qi , Cai, Jiaxin , Luo, Jiexin , Yu, Yuanlong , Gu, Jason , Pan, Jia et al. Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (2) , 1708-1715 . |
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Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware context fusion based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present the alternating local enhancement module that restricts the negative impact of redundant information introduced from the contexts, and thus is endowed with the ability of fixing the locality-aware features to produce refined results. Furthermore, in comprehensive experiments, we demonstrate that our model outperforms other state-of-the-art methods in public benchmarks and verify the effectiveness of the proposed modules. Our released codes will be available at: https://github.com/liqiokkk/FCtL.
Keyword ：
Attention mechanism Attention mechanism Context-guided vision model Context-guided vision model Geo-spatial image segmentation Geo-spatial image segmentation Ultra-high resolution image segmentation Ultra-high resolution image segmentation
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| GB/T 7714 | Liu, Wenxi , Li, Qi , Lin, Xindai et al. Ultra-High Resolution Image Segmentation via Locality-Aware Context Fusion and Alternating Local Enhancement [J]. | INTERNATIONAL JOURNAL OF COMPUTER VISION , 2024 , 132 (11) : 5030-5047 . |
| MLA | Liu, Wenxi et al. "Ultra-High Resolution Image Segmentation via Locality-Aware Context Fusion and Alternating Local Enhancement" . | INTERNATIONAL JOURNAL OF COMPUTER VISION 132 . 11 (2024) : 5030-5047 . |
| APA | Liu, Wenxi , Li, Qi , Lin, Xindai , Yang, Weixiang , He, Shengfeng , Yu, Yuanlong . Ultra-High Resolution Image Segmentation via Locality-Aware Context Fusion and Alternating Local Enhancement . | INTERNATIONAL JOURNAL OF COMPUTER VISION , 2024 , 132 (11) , 5030-5047 . |
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Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists’ subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards “next-generation diagnostic pathology”, prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.
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| GB/T 7714 | Shu Wang , Junlin Pan , Xiao Zhang et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy [J]. | Light:Science & Applications , 2024 , 13 (1) . |
| MLA | Shu Wang et al. "Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy" . | Light:Science & Applications 13 . 1 (2024) . |
| APA | Shu Wang , Junlin Pan , Xiao Zhang , Yueying Li , Wenxi Liu , Ruolan Lin et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy . | Light:Science & Applications , 2024 , 13 (1) . |
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Nuclei segmentation and classification play a crucial role in pathology diagnosis, enabling pathologists to analyze cellular characteristics accurately. Overlapping cluster nuclei, misdetection of small-scale nuclei, and pleomorphic nuclei-induced misclassification have always been major challenges in the nuclei segmentation and classification tasks. To this end, we introduce an auxiliary task of nuclei boundary-guided contrastive learning to enhance the representativeness and discriminative power of visual features, particularly for addressing the challenge posed by the unclear contours of adherent nuclei and small nuclei. In addition, misclassifications resulting from pleomorphic nuclei often exhibit low classification confidence, indicating a high level of uncertainty. To mitigate misclassification, we capitalize on the characteristic clustering of similar cells to propose a locality-aware class embedding module, offering a regional perspective to capture category information. Moreover, we address uncertain classification in densely aggregated nuclei by designing a top-k uncertainty attention module that leverages deep features to enhance shallow features, thereby improving the learning of contextual semantic information. We demonstrate that the proposed network outperforms the off-the-shelf methods in both nuclei segmentation and classification experiments, achieving the state-of-the-art performance. © 2024 Elsevier Ltd
Keyword ：
Classification (of information) Classification (of information) Computer aided diagnosis Computer aided diagnosis Deep learning Deep learning Image classification Image classification Semantics Semantics Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Liu, Wenxi , Zhang, Qing , Li, Qi et al. Contrastive and uncertainty-aware nuclei segmentation and classification [J]. | Computers in Biology and Medicine , 2024 , 178 . |
| MLA | Liu, Wenxi et al. "Contrastive and uncertainty-aware nuclei segmentation and classification" . | Computers in Biology and Medicine 178 (2024) . |
| APA | Liu, Wenxi , Zhang, Qing , Li, Qi , Wang, Shu . Contrastive and uncertainty-aware nuclei segmentation and classification . | Computers in Biology and Medicine , 2024 , 178 . |
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